Yoga for symptom management in oncology: A review of the evidence base and future directions for research
Bibliographic record
Abstract
Because yoga is increasingly recognized as a complementary approach to cancer symptom management, patients/survivors and providers need to understand its potential benefits and limitations both during and after treatment. The authors reviewed randomized controlled trials (RCTs) of yoga conducted at these points in the cancer continuum (N = 29; n = 13 during treatment, n = 12 post-treatment, and n = 4 with mixed samples). Findings both during and after treatment demonstrated the efficacy of yoga to improve overall quality of life (QOL), with improvement in subdomains of QOL varying across studies. Fatigue was the most commonly measured outcome, and most RCTs conducted during or after cancer treatment reported improvements in fatigue. Results also suggested that yoga can improve stress/distress during treatment and post-treatment disturbances in sleep and cognition. Several RCTs provided evidence that yoga may improve biomarkers of stress, inflammation, and immune function. Outcomes with limited or mixed findings (eg, anxiety, depression, pain, cancer-specific symptoms, such as lymphedema) and positive psychological outcomes (such as benefit-finding and life satisfaction) warrant further study. Important future directions for yoga research in oncology include: enrolling participants with cancer types other than breast, standardizing self-report assessments, increasing the use of active control groups and objective measures, and addressing the heterogeneity of yoga interventions, which vary in type, key components (movement, meditation, breathing), dose, and delivery mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".